Generalization error bounds for classifiers trained with interdependent data

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Nicolas Usunier, Massih R. Amini, Patrick Gallinari


In this paper we propose a general framework to study the generalization properties of binary classifiers trained with data which may be depen- dent, but are deterministically generated upon a sample of independent examples. It provides generalization bounds for binary classification and some cases of ranking problems, and clarifies the relationship between these learning tasks.